This paper develops a hybrid algorithm of adaptive network-based fuzzy inference system (ANFIS) and regions with convolutional neural network (R-CNN) for stereo vision-based object recognition and manipulation. The stereo camera at an eye-to-hand configuration firstly captures the image of the target object. Then, the shape, features, and centroid of the object are estimated. Similar pixels are segmented by the image segmentation method, and similar regions are merged through selective search. The eye-to-hand calibration is based on ANFIS to reduce computing burden. A six-degree-of-freedom (6-DOF) robot arm with a gripper will conduct experiments to demonstrate the effectiveness of the proposed system.

To achieve Industry 4.0 solutions for the networking of mechatronic components in production plants, the use of Internet of Things (IoT) technology is the optimal way for goods transportation in the cyber-physical system (CPS). As a result, automated guided vehicles (AGVs) are networked to all other participants in the production system to accept and execute transport jobs. Accurately tracking the planned paths of AGVs is therefore essential. The omnidirectional mobile vehicle has shown its excellent characteristics in crowded environments and narrow aisle spaces. However, the slip problem of the omnidirectional mobile vehicle is more serious than that of the general wheeled mobile vehicle. This paper proposes a slip estimation and compensation control method for an omnidirectional Mecanum-wheeled automated guided vehicle (OMWAGV) and implements a control system. Based on the slip estimation and compensation control of the general wheeled mobile platform, a Microchip dsPIC30F6010A microcontroller-based system uses an MPU-9250 multi-axis accelerometer sensor to derive the longitudinal speed, transverse speed, and steering angle of the omnidirectional wheel platform. These data are then compared with those from the motor encoders. A linear regression with a recursive least squares (RLS) method is utilized to estimate real-time slip ratio variations of four driving wheels and conduct the corresponding compensation and control. As a result, the driving speeds of the four omnidirectional wheels are dynamically adjusted so that the OMWAGV can accurately follow the predetermined motion trajectory. The experimental results of diagonally moving and cross-walking motions without and with slip estimation and compensation control showed that, without calculating the errors occurred during travel, the distances between the original starting position to the stopping position are dramatically reduced from 1.52 m to 0.03 m and from 1.56 m to 0.03 m, respectively. The higher tracking accuracy of the proposed method verifies its effectiveness and validness.

According to the data from Alzheimer’s Disease International (ADI) in 2018, it is estimated that 10 million new dementia patients will be added worldwide, and the global dementia population is estimated to be 50 million. Due to a decline in the birth rate and the development and great progress of medical technology, the proportion of elderly people has risen annually in Taiwan. In fact, Taiwan has become one of the fastest-growing aged countries in the world. Consequently, problems related to aging societies will emerge. Dementia is one of most prevailing aging-related diseases, with a great influence on daily life and a great economic burden. Dementia is not a single disease, but a combination of symptoms. There is currently no medicine that can cure dementia. Finding preventive measures for dementia has become a public concern. Older people should actively increase brain-protective factors and reduce risk factors in their lives to reduce the risk of dementia and even prevent the occurrence of dementia. Studies have shown that engaging in mental or creative activities that stimulate brain function has a relative risk reduction of nearly 50%. Elderly people should develop the habit of life-long learning to strengthen effective neural bonds between brain cells and preserve brain cognitive functions. Playing chess is one of the suggested activities. This paper aimed to develop a Chinese robotic chess system for the elderly. It mainly uses a camera to capture the contour of the Chinese chessman, recognizes the character and location of the chessman, and then transmits this information to the robotic arm, which will grab and place the chessman in the appropriate position on the chessboard. The camera image is transmitted to MATLAB for image recognition. The character of the chessman is recognized by convolutional neural networks (CNNs). Forward and inverse kinematics are used to manipulate the robotic arm. Even if the chessmen are arbitrarily placed, the experiment showed that their coordinates can be found through the camera as long as they are located within the working scope of the camera and the robotic arm. For black chessmen, no matter how many degrees they are rotated, they can be recognized correctly, while the red ones can be recognized 100% of the time within 90° of rotation and 98.7% with more than a 90° rotation.

Automated guided vehicles (AGVs) have become prevalent over the last decade. However, numerous challenges remain, including path planning, security, and the capacity to operate safely in unstructured environments. This study proposes an obstacle avoidance system that leverages deep action learning (DAL) to address these challenges and meet the requirements of Industry 4.0 for AGVs, such as speed, accuracy, and robustness. In the proposed approach, the DAL is integrated into an AGV platform to enhance its visual navigation, object recognition, localization, and decision-making capabilities. Then DAL itself was introduced to combine the work of You Only Look Once (YOLOv4), speeded-up robust features (SURF), and k-nearest neighbor (kNN) and AGV control in indoor visual navigation. The DAL system triggers SURF to differentiate two navigation images, and kNN is used to verify visual distance in real time to avoid obstacles on the floor while searching for the home position. The testing findings show that the suggested system is reliable and fits the needs of advanced AGV operations.

Robotic grasping for cluttered tasks and heterogeneous targets is not satisfied by the deep learning that has been developed in the last decade. The main problem lies in intelligence, which is stagnant, even though it has a high accuracy rate in usual environment; however, the cluttered grasping environment is very irregular. In this paper, an action learning for robotic grasping using eye-in-hand coordination is developed to grasp the cluttered and wide range of various objects using 6 degree-of-freedom (DOF) robotic manipulator equipped with a three-finger gripper. To involve action learning in this system, k-Nearest Neighbor (kNN), Disparity Map (DM), and You Only Look Once (YOLO) were needed. After successfully formulating the learning cycle, an instrument assesses the robot’s environment and performance with qualitative weightings. Some experiments of measuring the depth of the target, localization of target variations, target detection, and the gripping process itself were conducted. The entire process is spread out in plan, act, observe, and reflect for each action learning cycle. If the first cycle does not suffice the results according to the minimum pass standard, the cycle will renew until the robot succeeds in picking and placing. Furthermore, this study demonstrated that the action learning-based object manipulation system with stereo-like vision and eye-in-hand calibration can improve intelligence over previous errors with acceptable errors. Thus, action learning might be applicable to other object manipulation systems without having to define the environment first.

In the future, microhybrid power plant for the household scale will be something that the people want. The obstacle that is faced by the developers in micro hybrid plant development is the lack of a preliminary study. If a preliminary study is not possible, at least the developer should conduct a potential survey to determine the place where the plant will be installed. Optimal position for the hybrid power plant can be obtained through a continuous mapping at some point during the season span. This study uses research and development procedures of the internet of things (IoT) for its purpose of survey of renewable energy potential. The aim of this study is to obtain the real time data of the potential of renewable energy with the help of the IoT. Internet of things which is developed using Airlink FXT009 EDGE is operated via serial communication. Modem Airlink FXT009 is controlled by ATmega328 microcontroller which received its data from the sensor network. The database which is processed by IoT consists of wind velocity, wind direction, output voltage, and stationidentity. The research shows that the average of wind velocity during April-September is 3.2 m/s and the output voltage is 13.3V.On the other hand, during October-March the wind velocity is 5.3m/s, and the average output voltage at 10:00-14:00 is 10.2V with the average of speed and data updates is per 75.34 seconds. This study is limited to the ATmega328 microcontroller as the processor and need to be developed towards a mini PC.Keywords-internet of things (IoT), energy potential survey, hybrid power plant, ATMega328